This note talks about using a method called segmented multinomial logit models to analyze data from choice experiments. The researchers show how they can divide the data into segments using a specific framework and software. They then compare the results of this method with another model used in a previous study. By segmenting the data, they get results that are similar to the other model but easier to understand. The researchers discuss the strengths and weaknesses of this approach and suggest areas for future research.